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Linear scaling relationships and volcano plots in
homogeneous catalysis – revisiting the Suzuki
reaction†
Michael Busch, Matthew D. Wodrich and Clémence Corminboeuf*
Linear free energy scaling relationships and volcano plots are common tools used to identify potential
heterogeneous catalysts for myriad applications. Despite the striking simplicity and predictive power of
volcano plots, they remain unknown in homogeneous catalysis. Here, we construct volcano plots to
analyze a prototypical reaction from homogeneous catalysis, the Suzuki cross-coupling of olefins.
Volcano plots succeed both in discriminating amongst different catalysts and reproducing experimentally
Received 7th August 2015
Accepted 1st September 2015
known trends, which serves as validation of the model for this proof-of-principle example. These
findings indicate that the combination of linear scaling relationships and volcano plots could serve as a
DOI: 10.1039/c5sc02910d
valuable methodology for identifying homogeneous catalysts possessing a desired activity through a
www.rsc.org/chemicalscience
priori computational screening.
Introduction
Nobel laureate Paul Sabatier conceived of an “ideal catalyst” in
which interactions with a substrate are tuned to be neither too
weak nor too strong.1 This phenomenon, commonly known as
Sabatier's principle, has been cast into an intuitive tool, volcano
plots, which pictorially characterize catalytic activity with
respect to catalyst/intermediate interactions.2,3 The predictive
power of these concepts has rendered them indispensible in
modern heterogeneous catalysis and electrochemistry.4–6
Volcano plots contain a minimum of two slopes, meeting in a
top. The volcano shape aids comparison of the thermodynamics
between different catalysts, thereby facilitating identication of
“good” candidates. Thermodynamically optimal candidates,
those fullling Sabatier's principle, appear near the highest
point of the volcano. The volcano slopes delineate situations in
which the catalyst/substrate interaction is either too strong (le
slope) or too weak (right slope). In computational chemistry,
volcano plots are oen constructed from linear free energy
scaling relationships,7 which indicate that the relative stability
of intermediates are dependent on one another.8,9
Given their ability to identify attractive catalysts as well
as their conceptual simplicity, the idea of importing volcano
plots from the heterogeneous community to the realm of
Laboratory for Computational Molecular Design, Institute of Chemical Sciences and
Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne,
Switzerland. E-mail: clemence.corminboeuf@ep.ch
† Electronic supplementary information (ESI) available: Detailed derivation of the
linear scaling relationships and construction of the volcano plots as well as
comparisons of computed values using PBE0-dDsC and M06 functionals is
included. See DOI: 10.1039/c5sc02910d
6754 | Chem. Sci., 2015, 6, 6754–6761
homogeneous catalysis is wholly attractive. Indeed, many of the
underlying principles of volcanoes, namely linear scaling relationships, are longstanding concepts associated with physical
organic chemistry (e.g., Hammett equation,10 Bell–Evans–Polyani principle11,12) and homogeneous catalysis (e.g., Brønsted
catalysis equation13) and are routinely used today in both the
heterogeneous14 and homogeneous15 communities. Despite
this, to the best of our knowledge, volcano plots for homogeneous systems have only been proposed in a hypothetical
sense,16 but never brought into concrete existence. Here, we
combine linear free energy scaling relationships and volcano
plots to re-examine a prototypical and well-studied reaction
from homogeneous catalysis, the Suzuki cross-coupling17–19 of
olens (eqn (1)). This reaction was chosen in order to establish
the viability of volcano plots as a tool for use in homogeneous
catalysis. Validation requires determining the ability of volcanoes to reproduce experimentally determined data and trends
for a restricted set of catalysts on a well-studied system. For this
purpose, Suzuki coupling seems particularly appropriate, given
the considerable amount of knowledge and understanding
gained during the decades since its introduction. We stress that
the primary objective of this work is not to predict new catalysts
for the Suzuki coupling of olens, but rather to denitively
establish that volcano plots are capable of identifying thermodynamically attractive catalysts for homogeneous reactions.
Only aer this key objective has been unambiguously established can studies be extended to a broader scope of catalysts
and other homogeneous reactions.
½Pd
R1 X þ R2 BðORÞ2 ƒƒƒƒ! R1 R2 þ XBðORÞ2
(1)
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Fig. 1
Reaction mechanism for the Suzuki cross-coupling of olefins.
The Suzuki reaction involves the coupling of an aryl or vinyl
halogenide (R1–X) with an organoborate [R2B(OR)2] to form R1–
R2 using a Pd catalyst (eqn (1)).17–19 The now well-established
reaction mechanism20–24 proceeds as depicted in Fig. 1 with
oxidative addition (Rxn A), cis/trans isomerisation (Rxn B),
ligand exchange (Rxn C), transmetallation (Rxn D), trans/cis
isomerisation (Rxn E), and reductive elimination (Rxn F) steps.
During this cycle the catalyst proceeds through a series of ve 16
electron square planar intermediates (2–6), the relative stabilities of which will become the basis of the linear scaling relationships (vide infra). While the reaction is known to proceed
through these particular intermediates detailed knowledge of
how the specic transitions occur between these intermediates
is not necessary for creating insightful volcano plots.
Here, the manner in which different metal/ligand combinations inuence the thermodynamics of the Suzuki reaction
are probed using density functional theory computations.
Because the goal of this work is establishing that the concepts of
linear scaling relationships and volcano plots are as valuable for
homogeneous systems as they are for predicting heterogeneous
catalysts, we focus on simpler illustrative systems that demonstrate and reinforce these points. Note that several of these
systems are expected to perform poorly, while others are
expected to perform well. Taken together, these two limiting
cases should effectively validate the power of volcano plots. Six
metals (Ni, Pd, Pt, Cu, Ag, and Au) were combined with six
ligand sets [CO (x2), NH3 (x2), PMe3 (x2), acetone (x2), an Nheterocyclic carbene (x2), and a mixed PMe3/NH3 system], the
combinations of which produced 36 potential catalysts. To align
with the chemistry of the Suzuki reaction, the oxidation states of
the catalysts were adjusted to comply with the known 14e/16e
nature of the complexes. Of the 36 systems evaluated, 27 had
stationary points for all catalytic cycle intermediates, while ve
had stationary points for only some intermediates. All of these
species were used to establish linear scaling relationships.
Computational details
Vinylbromide (H2CCHBr) and [H2CCH(OtBu)(OH)2] were used
as coupling partners and NaOtBu and NaBr for ligand exchange.
Geometries of all species were obtained by optimizations using
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the M06 (ref. 25 and 26) density functional along with the def2SVP27 basis set in implicit THF solvent (SMD28 solvation model)
with the “Ultrane” integration grid in Gaussian 09.29 Rened
energies were obtained by single point energy computations
using a density-dependent dispersion correction30–33 (-dDsC)
coupled with the PBE0 (ref. 34 and 35) functional along with the
triple-z Slater type orbital basis set (TZ2P) in ADF.36,37 Final
solvation corrections (also in THF) were determined using
COSMO-RS,38 as implemented in ADF. Reported free energies
include unscaled free energy corrections from M06/def2-SVP
computations. Note that the combination of the -dDsC
density dependent dispersion correction with COSMO-RS, as
well as other solvation models, has been successfully used in
numerous applications of catalysis with metal centres.39–43
The validity of the computational methodology was further
conrmed via favourable comparisons with computations using
the M06 functional combined with the SMD solvation model
(e.g., the same catalysts were identied as the most attractive
thermodynamic candidates independent of functional choice,
see ESI†). Selected species were also computed using alternate
spin states, which revealed that the closed-shell singlet represented the ground state in all cases.
While we used static DFT computations as a tool to illustrate
the ability of volcano plots to reproduce experimentally known
trends from homogeneous catalysis, in principle, any number
of computational or experimental techniques could also be
employed. Since we tended to choose small, non-exible ligands
for our catalysts static DFT computations are appropriate. It
could be foreseen, however, that larger, more bulky ligands
residing on a catalyst (as oen employed in experimental
settings) might introduce problems arising from describing the
free energy of a Boltzmann like conformer distribution using a
single structure. In such a case, obtaining free energies from
MD simulations, which specically include the inuences of
conformational entropy,44 would be a tting alternative.
Results and discussion
Free energy plots
Fig. 2 depicts computed free energy diagrams describing the
reaction energetics of Suzuki coupling for three exemplary
catalytic systems. From a thermodynamic perspective, an ideal
catalyst would proceed through the Fig. 1 catalytic cycle via a
series of equally exergonic reaction steps, thereby making each
intermediary reaction equally facile, assuming thermodynamic
control. Such a situation, on average, would minimize the
reverse reaction rate for each individual step and drive the
system in a consistent manner toward the products (dotted
lines, Fig. 2). Of course, this situation seldom occurs; for the
particular case of Suzuki coupling no “ideal catalyst” is
observed. Instead, the behaviour of different catalysts deviates,
to a greater or lesser degree, depending on their specic properties. The largest deviations from the behaviour of a hypothetical ideal catalyst (dotted lines, Fig. 2) tend to appear in the
oxidative addition and reductive elimination intermediary
steps. While other metal/ligand combinations exhibit more
extreme behaviour (see ESI† for more dramatic cases) the trends
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Fig. 2 Free energy plots (PBE0-dDsC/TZ2P//M06/def2-SVP,
COSMO-RS solvation) relative to the resting state (DGRRS, defined by
eqn (2)–(6)) for selected catalysts: (a) Pd(CO)2, (b) Pd(NH3)2, (c)
Pd(PMe3)2. Moving between different species (1–6) corresponds to
completing the corresponding reactions (A–F) given in Fig. 1.
amongst several Pd based catalysts more typically associated
with Suzuki coupling are quite illustrative.
For example, Pd(CO)2 is a representative case of a catalyst
where the intermediate species are destabilized. This situation
is dened by intermediates lying above the “ideal” line, such as
in the Fig. 2a plot. Catalysts with destabilized intermediates
have oxidative addition steps that are generally less exergonic
then for the hypothetical ideal catalyst. For Pd(CO)2, the value of
oxidative addition is only 5.9 kcal mol1, considerably less
6756 | Chem. Sci., 2015, 6, 6754–6761
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than the 11.3 kcal mol1 of the hypothetical ideal catalyst. The
contrasting case, where intermediates are over stabilized, is
seen in Pd(NH3)2 (Fig. 2b). Here oxidative addition is strongly
exergonic, with a value of 44.0 kcal mol1, greatly exceeding
the ideal 11.3 kcal mol1 of the hypothetical ideal catalyst.
This strongly exergonic intermediary reaction causes most
intermediates to fall below the “ideal” line. Because the free
energy to complete one catalytic cycle is xed (67.9 kcal mol1
for the Suzuki coupling studied here) an oxidative addition step
that is either overly or underly exergonic must be balanced
elsewhere in the catalytic cycle. This energetic compensation is
seen in the cycle's ultimate step, reductive elimination. Systems
with destabilized intermediates (e.g., weakly exergonic oxidative
addition) tend to have strongly exergonic reductive elimination
steps and vice versa. These cases are again exemplied by
Pd(CO)2 (Fig. 2a) with destabilized intermediates and Pd(NH3)2
(Fig. 2b) with over stabilized intermediates. In contrast to those
cases, the thermodynamics of Pd(PMe3)2 (Fig. 2c) more closely
follow the “ideal” line, as expected. Note that the energetics of
the oxidative addition and reductive elimination steps more
closely align (24.0 and 27.8 kcal mol1, respectively), which
should assist in driving the catalytic cycle forward in a consistent manner.
It is important to remember that this picture only considers
the thermodynamics of the catalytic cycle and ignores kinetic
aspects. Of course, it is well understood that the difference
between a “good” and “bad” catalyst oen depends upon the
barrier heights associated with movements between intermediates. This is particularly true for establishing reaction enantioselectivity, where the nal products depend upon the
detailed kinetics of each system. For the purposes of initial
characterization of catalysts it is assumed that the system is
under thermodynamic control. Within the context of volcano
plots, the rst priority is validating system thermodynamics,
which determine the plausibility of a reaction proceeding for a
given catalyst. Since comparable scaling relations between free
energies and barrier heights have been identied, similar plots
that explicitly incorporate activation barriers could be envisioned. Indeed, Bell–Evans–Polanyi scaling relations, which
relate thermodynamics with kinetics, have been used in
heterogeneous catalysis45,46 and should also be appropriate for
homogeneous systems. Alternatively, the kinetics of a handful
of systems identied by volcano plots as being the most thermodynamically appealing could be examined in more detail. In
this manner, a great deal of time is saved since systems with
poor thermodynamics are excluded from the onset.
Linear free energy scaling relationships
When a sufficiently large number of catalysts are screened for
a particular reaction, it becomes possible to establish whether
linear scaling relationships exist. Assuming a sufficiently
good correlation, these relationships permit the description
of the stability of an intermediate with respect to the relative
stability of a descriptor intermediate. For example, Fig. 3a
shows that the DGRRS of intermediates 3 and 2 correlate
extremely strongly with one another (R2 ¼ 0.98), thereby
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(5)
(6)
Linear scaling relationships amongst intermediates. Free
energies (DGRRS) are relative to the catalytic resting state, as defined by
eqn (2)–(6). Equations defining the liner scaling relationships (upper
left) are used to create volcano plots (vide infra). No comparison
between 1 and 3 is possible, since DGRRS(1) is, by definition, zero.
Comparisons with 3 are equal to unity. Red points in (d) have been
excluded from the data fitting equations based on Grubbs' statistical
test for outliers. Note that equations derived from the linear scaling
relationships appear to be independent of computational level (see
ESI† for details).
Fig. 3
allowing DGRRS(2) to be cast in terms of DGRRS(3)47 [DGRRS(3)
¼ DGRRS(2) + 3 kcal mol1]. Aside from the convenient ability
to describe the DG of one intermediate in terms of another,
this mathematical relationship also has direct chemical
meaning, where the slope of unity indicates similar bonding
patterns and the y-intercept of +3 kcal mol1 indicates that,
on average, the DGRRS(3) lies 3 kcal mol1 higher in energy
than DGRRS(2). Similar extremely strong correlations are seen
between DGRRS(3) and DGRRS(4) (Fig. 3b, R2 ¼ 0.99), DGRRS(5)
(Fig. 3c, R2 ¼ 0.98) and DGRRS(6) (Fig. 3d, R2 ¼ 0.93). Owing to
these strong correlations, it becomes possible to describe the
average expected value of each intermediates entirely in terms
of DGRRS(3). The importance of these relationships cannot be
over stressed; as these descriptors will later dene the volcano
plots (vide infra). One minor shortcoming is that these scaling
relations describe only electronic effects of the system. This
current limitation would make description of catalysts with
bulky ligands where steric interactions are used to drive
reaction enantioselectivity difficult. Accordingly, corrections
for such effects should be considered in the future.
(2)
(3)
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The y-intercepts of the linear scaling free energy relationships can further serve to identify any energetically problematic
steps between catalytic cycle intermediates. These would be
associated with large positive y-intercepts, which indicate
signicant thermodynamic barriers.48 Large y-intercepts of this
type are absent for the Suzuki reaction. The small intercept
values given by the relationships between 3 and 2 as well as 3
and 4 indicate that these intermediates, on average, lie energetically near one another. On the other hand, the large negative
y-intercepts for the linear scaling relationships between 3 and 5
as well as 3 and 6 indicate that these later steps lie signicantly
lower in energy than intermediate 3, which should help drive
the catalytic cycle toward completion. From the linear scaling
relationships derived in Fig. 3 it is expected that this particular
reaction should proceed smoothly through the intermediates
without any major thermodynamic barrier.
Construction of volcano plots
Having established the existence of linear scaling relationships
amongst the catalytic cycle intermediates, volcano plots, which
aid in the identication of thermodynamically attractive
candidates, can be constructed. The basic premise of such plots
is to illustrate relationships between the catalytic cycle reaction
free energies (DGRxn, y-axis) and the stability of a chosen intermediate species relative to the catalytic resting state (DGRRS, xaxis). Lines dening reaction energies are obtained from the
previously derived linear scaling relationships (upper le corner
of Fig. 3 plots). Because the scaling relation slopes are equivalent for intermediates 2–6 (see Fig. 3), reactions that successively transition between these intermediates (B–E) appear as
horizontal lines, i.e., the reaction free energy is independent
from the choice of catalyst for these steps. Reactions A (oxidative addition) and F (reductive elimination), however, appear as
sloped lines owing to their dependence on DGRRS(3) (see
Fig. 4a), the descriptor intermediate. Detailed explanations and
derivations of the Fig. 4a equations are provided in the ESI.†
While Fig. 4a shows the average reaction free energies (A–F)
relative to DGRRS(3) obtained from the linear scaling relationships, the volcano plots are dened in terms of a “potential
determining step” DG(pds), determined by eqn (7). Pictorially,
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Fig. 4 (a) Plot of linear scaling relationships, derived from intermediates ((reactions B–E), depicted as straight lines) from the corresponding linear relationships (Fig. 3). Reactions A (oxidative addition)
and F (reductive elimination) appear as sloped lines. (b) Volcano plot
derived from (a). Reactions C and E are the potential determining step
for the plateau region. Lines defining the volcano are obtained by
taking the lowest DG(pds) value amongst all reactions for each
DGRRS(3) value.
the reaction line (A–F, Fig. 4a) with the most negative (or least
positive) DG(pds) value for any DGRRS denes the potential
determining step. An examination of Fig. 4a reveals that reaction F has the lowest values for DGRRS(3) values less than 50
kcal mol1; both reactions C (ligand exchange) and E (trans/cis
isomerization) have the most negative DG(pds) values for
DGRRS(3) between 50 and 10 kcal mol1; and reaction A
has the most negative values for DGRRS(3) quantities greater
than 10 kcal mol1. Taking only the reaction lines with the
lowest values for DGRRS(3) gives the shape of the volcano plot,
Fig. 4b.
DG(pds) ¼ max[DGRxn(A), DGRxn(B), DGRxn(C),
DGRxn(D), DGRxn(E), DGRxn(F)]
(7)
For characterization purposes, this volcano plot can be
subdivided into three sections: the le slope, conventionally
referred to as the “strong binding side” (I, Fig. 4b) where
intermediates are overly stabilized relative to the “hypothetical
ideal catalyst” (e.g., dotted lines, Fig. 2), and reductive elimination (reaction F) is potential determining, a “weak binding
side” (III, Fig. 4b) with under or destabilized intermediates,
making oxidative addition (reaction A) potential determining,
and the plateau region where the free energies associated with
6758 | Chem. Sci., 2015, 6, 6754–6761
oxidative addition and reductive elimination are roughly
balanced (II, Fig. 4b). It is in this nal area that catalysts having
the most appealing thermodynamic proles fall. Distinguishing
thermodynamically attractive catalysts is then remarkably
simple; those with the largest DG(pds) values (e.g., higher on
or above the volcano) are the most attractive since they have the
most exergonic reaction free energy for the potential determining step.
Fig. 5 presents the volcano constructed from the linear scaling
relationships in Fig. 4b, with points representing the individual
catalysts now included. The location of the each catalyst in one of
the three dening regions (I–III) is determined solely by its value
of DGRRS(3), making creation of the nal volcano plot quite easy. A
closer examination immediately reveals the superior performance
of group 10 metal catalysts (Ni, Pd, Pt) relative to those possessing
coinage metal centres (Cu, Ag, Au).49 The later catalysts appear
uniformly on the volcano's “weak binding side”, which aligns with
known difficulties involving oxidative addition for gold and silver
catalysts.50 While it may have been possible to make this prediction in advance based on chemical knowledge and intuition, it is
an important point and critical validation of the model that the
volcano plot is able to reproduce these experimental observations
without requiring any knowledge of behaviour of these catalysts.
While it is simple to characterize differences between catalysts with group 10 and 11 metal centres based on the Fig. 5
volcano, distinguishing amongst the different group 10 metal
catalysts is more difficult. It should be noted, however, that
several nickel catalysts (depicted in green) are predicted to
perform well, which is attractive from the perspective of using
earth abundant metals for catalysis.51 Ligand inuences are also
clearly seen. Considering a catalyst on the strong binding side
(region I) of the volcano, Ni(NH3)2, destabilizing the intermediates will shi a Ni catalyst into region II. This can be achieved
by replacing ammonia with CO ligands, which succeed in not
only destabilizing the intermediates into region II but also
increasing the exergonicity of the potential determining step.
Volcano plot illustrating the thermodynamic suitability of
potential catalysts derived from Fig. 4. Reaction E is the potential
determining step for the plateau region. Lines defining the volcano are
obtained by taking the lowest DG(pds) value amongst all reactions
(A–F) for each DGRRS(3) value.
Fig. 5
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The Fig. 5 volcano plot corresponds to a situation in which
only the mechanism of the catalytic cycle is known. Given this
same information, volcano plots can be constructed and
applied to nearly any reaction from homogeneous catalysis. Of
course, chemists are not only interested in developing new
reactions, but also frequently search for more efficient, cheaper,
or more environmentally friendly catalysts for catalytic
processes with rich and well-established chemistries. In these
instances a great deal of information regarding, for example,
the ease and speed of transformation between different intermediates may have been garnered from experimental studies.
Valuable information of this type can also been incorporated
into volcano plots and assist in predicting a more rened set of
catalytic candidates. For Suzuki coupling the isomerization
(reactions B and E) and ligand exchange (reaction C) steps are
known to occur relatively rapidly compared to the transmetallation step in the laboratory,52,53 meaning that they are
highly unlikely to be potential determining for the catalytic
cycle. Therefore, it is reasonable to eliminate these as possible
potential determining steps and create a new volcano plots in
which reactions B, C, E have been removed (Fig. 6, see ESI† for
details). This leaves transmetallation (reaction D), rather than
ligand exchange (reaction C) or cis/trans isomerization (reaction
E) to dene the plateau of the rened volcano (Fig. 6b). The
Fig. 6 (a) Plot of linear scaling relationships, derived from intermediates where reactions B, C, and E have been removed because they are
known to occur rapidly based on experimental observations. Reactions A (oxidative addition) and F (reductive elimination) appear as
sloped lines. (b) Volcano plot derived from (a). Reaction D is the
potential determining step for the plateau region. Lines defining the
volcano are obtained by taking the lowest DG(pds) value amongst all
reactions for each DGRRS(3) value.
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Refined volcano plot illustrating the thermodynamic suitability
of potential catalysts where the isomerization and ligand exchange
steps are ignored. The most attractive candidates lie near the top of the
volcano in region II. Equations used to derive the volcano are presented in the ESI.†
Fig. 7
rened volcano plot including points for the individual catalysts
in shown in Fig. 7. Here, in comparison to the Fig. 5 volcano
plot, the number of catalysts appearing in the thermodynamically “well-balanced” plateau region is greatly reduced. Of these
ve attractive candidates, three incorporate a palladium centre
[Pd(PMe3)2, Pd(acetone)2, Pd(NH3) (PMe3)], one has a nickel
centre, Ni(CO)2, and the nal is Pd(PPh3)2, which will be discussed shortly. Gratifyingly, the truncated version of Suzuki's
original catalyst is identied as the most attractive thermodynamic candidate, represented by its placement above the
plateau. Likewise, Pd(carbene)2, while not located in region II of
Fig. 7, does lie relatively high on the volcano. The location of
this particular species in region I does, however, indicate that
reductive elimination may sometimes be problematic for this
species. Overall, the high ranking of the phosphine and carbene
catalysts further validates the conceptual use of volcano plots
for identifying thermodynamically attractive homogeneous
catalysts.
Now that concept of using linear scaling relationships to
create volcano plots has been demonstrated for homogeneous
catalysis, it is important to explain how previously constructed
volcano plots could easily be used to determine the viability of
new catalysts, particularly for new, less studied reactions. The
thermodynamics of a potential catalyst can be assessed by
computing the free energies of only four intermediates, which
determine the binding (DGRRS) and reaction energies (DGRxnA,
etc.) necessary to place a catalysts onto the volcano plot. Using
Suzuki coupling as an example, it would rst be necessary to
compute the energies of 1 and 3 (plus vinylbromide) that
provides the value of DGRRS(3). This value determines which of
the potential determining steps [DG(pds) for regions I, II, or
III, Fig. 7], governs this catalyst. For the sake of argument, let us
assume that the DGRRS(3) value lies within region II (Fig. 7). It
would then be necessary to determine the energies of 4 and 5
(plus the requisite boron compounds) to calculate the DG(pds)
corresponding to reaction D. DGRRS(3) values falling in other
regions (e.g., I or III) require the determination of the energies
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of other intermediates. The new catalyst can then be placed
onto a previously determined volcano using the DGRRS and
DG(pds) as a set of Cartesian coordinates. In this way, the
screening of new catalysts represents a signicant computational speed up, as it is not necessary to compute the entire
catalytic cycle. Moreover, the initial volcano plot can be established using a set of simpler ligands for which the complete
catalytic cycle can be computed more rapidly. Catalysts bearing,
for example, larger and more exotic ligands, can then be
assessed via the computationally reduced procedure described
directly above.
To illustrate this point, we computed the four necessary
intermediates [1, 2, 4, and 5, as the DGRRS(3) places this catalyst
in region II of Fig. 7] necessary to place Pd(PPh3)2 onto the
volcano plot (Fig. 7, pink triangle). As expected, Pd(PPh3)2 lies
high on the volcano, consistent with its known efficacy for
Suzuki coupling. We emphasize to determine this there was no
need to compute the entire catalytic cycle. This example illustrates the way in which catalytic screening using existing
volcano plots constructed based on simpler ligands can
proceed. In this manner, it is envisioned that volcano plots
based on linear scaling relationships could become a valuable
tool for in silico catalytic screening.
Conclusion
We assessed the ability of linear scaling relationships and
volcano plots to reproduce known catalytic trends and artefacts
for the well-studied Suzuki reaction. This proof-of-principle
example shows that these commonly used tools borrowed from
the heterogeneous catalysis community succeed in reproducing
known trends and observations for homogeneous catalysis.
While the aim of this study was to validate and show functionality of the model, in the future studies employing this same
methodology have the potential to be extremely helpful for
identifying attractive homogeneous catalysts. Constructing
volcano plots based on computed data can serve as an important precursor step to the synthesis of new catalysts for myriad
chemical reactions through a priori computational screening
and identication of thermodynamically attractive candidates.
Acknowledgements
The National Center of Competence in Research (NCCR)
“Materials' Revolution: Computational Design and Discovery of
Novel Materials (MARVEL)” of the Swiss National Science
Foundation (SNSF) and the EPFL are acknowledged for nancial
support. Prof. Jérôme Waser (EPFL) is acknowledged for helpful
discussions and critical reading on the manuscript.
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